






import argparse

import evaluate
import torch
from datasets import load_dataset
from torch.optim import AdamW
from torch.utils.data import DataLoader
from transformers import AutoModelForSequenceClassification, AutoTokenizer, get_linear_schedule_with_warmup, set_seed

from accelerate import Accelerator, DistributedType


########################################################################
# This is a fully working simple example to use Accelerate
#
# This example trains a Bert base model on GLUE MRPC
# in any of the following settings (with the same script):
#   - single CPU or single GPU
#   - multi GPUS (using PyTorch distributed mode)
#   - (multi) TPUs
#   - fp16 (mixed-precision) or fp32 (normal precision)
#
# To run it in each of these various modes, follow the instructions
# in the readme for examples:
# https://github.com/huggingface/accelerate/tree/main/examples
#
########################################################################


a_		:    Optional[int]   							=     16
a_		:    Any   							=     32






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase = 16):
			SCREAMING_SNAKE_CASE							=			AutoTokenizer.from_pretrained('bert-base-cased')
			SCREAMING_SNAKE_CASE							=			load_dataset('glue'				,							'mrpc')

			def tokenize_function(_UpperCAmelCase):
						# max_length=None => use the model max length (it's actually the default)
						SCREAMING_SNAKE_CASE							=			tokenizer(examples['sentence1']				,							examples['sentence2']				,							truncation=_UpperCAmelCase				,							max_length=_UpperCAmelCase)
						return outputs

			# Apply the method we just defined to all the examples in all the splits of the dataset
			# starting with the main process first:
			with accelerator.main_process_first():
						SCREAMING_SNAKE_CASE							=			datasets.map(
						    _UpperCAmelCase				,							batched=_UpperCAmelCase				,							remove_columns=['idx', 'sentence1', 'sentence2']				,							)

			# We also rename the 'label' column to 'labels' which is the expected name for labels by the models of the
			# transformers library
			SCREAMING_SNAKE_CASE							=			tokenized_datasets.rename_column('label'				,							'labels')

			def collate_fn(_UpperCAmelCase):
						# On TPU it's best to pad everything to the same length or training will be very slow.
						SCREAMING_SNAKE_CASE							=			128 if accelerator.distributed_type == DistributedType.TPU else None
						# When using mixed precision we want round multiples of 8/16
						if accelerator.mixed_precision == "fp8":
									SCREAMING_SNAKE_CASE							=			16
						elif accelerator.mixed_precision != "no":
									SCREAMING_SNAKE_CASE							=			8
						else:
									SCREAMING_SNAKE_CASE							=			None

						return tokenizer.pad(
						    _UpperCAmelCase				,							padding='longest'				,							max_length=_UpperCAmelCase				,							pad_to_multiple_of=_UpperCAmelCase				,							return_tensors='pt'				,							)

			# Instantiate dataloaders.
			SCREAMING_SNAKE_CASE							=			DataLoader(
			    tokenized_datasets['train']				,							shuffle=_UpperCAmelCase				,							collate_fn=_UpperCAmelCase				,							batch_size=_UpperCAmelCase				,							drop_last=_UpperCAmelCase)
			SCREAMING_SNAKE_CASE							=			DataLoader(
			    tokenized_datasets['validation']				,							shuffle=_UpperCAmelCase				,							collate_fn=_UpperCAmelCase				,							batch_size=_UpperCAmelCase				,							drop_last=(accelerator.mixed_precision == 'fp8')				,							)

			return train_dataloader, eval_dataloader






def 					lowerCamelCase__					(_UpperCAmelCase				,							_UpperCAmelCase):
			# Initialize accelerator
			SCREAMING_SNAKE_CASE							=			Accelerator(cpu=args.cpu				,							mixed_precision=args.mixed_precision)
			# Sample hyper-parameters for learning rate, batch size, seed and a few other HPs
			SCREAMING_SNAKE_CASE							=			config['lr']
			SCREAMING_SNAKE_CASE							=			int(config['num_epochs'])
			SCREAMING_SNAKE_CASE							=			int(config['seed'])
			SCREAMING_SNAKE_CASE							=			int(config['batch_size'])

			SCREAMING_SNAKE_CASE							=			evaluate.load('glue'				,							'mrpc')

			# If the batch size is too big we use gradient accumulation
			SCREAMING_SNAKE_CASE							=			1
			if batch_size > MAX_GPU_BATCH_SIZE and accelerator.distributed_type != DistributedType.TPU:
						SCREAMING_SNAKE_CASE							=			batch_size // MAX_GPU_BATCH_SIZE
						SCREAMING_SNAKE_CASE							=			MAX_GPU_BATCH_SIZE

			set_seed(_UpperCAmelCase)
			SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			get_dataloaders(_UpperCAmelCase				,							_UpperCAmelCase)
			# Instantiate the model (we build the model here so that the seed also control new weights initialization)
			SCREAMING_SNAKE_CASE							=			AutoModelForSequenceClassification.from_pretrained('bert-base-cased'				,							return_dict=_UpperCAmelCase)

			# We could avoid this line since the accelerator is set with `device_placement=True` (default value).
			# Note that if you are placing tensors on devices manually, this line absolutely needs to be before the optimizer
			# creation otherwise training will not work on TPU (`accelerate` will kindly throw an error to make us aware of that).
			SCREAMING_SNAKE_CASE							=			model.to(accelerator.device)
			# Instantiate optimizer
			SCREAMING_SNAKE_CASE							=			AdamW(params=model.parameters()				,							lr=_UpperCAmelCase)

			# Instantiate scheduler
			SCREAMING_SNAKE_CASE							=			get_linear_schedule_with_warmup(
			    optimizer=_UpperCAmelCase				,							num_warmup_steps=100				,							num_training_steps=(len(_UpperCAmelCase) * num_epochs) // gradient_accumulation_steps				,							)

			# Prepare everything
			# There is no specific order to remember, we just need to unpack the objects in the same order we gave them to the
			# prepare method.

			SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			accelerator.prepare(
			    _UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase				,							_UpperCAmelCase)

			# Now we train the model
			for epoch in range(_UpperCAmelCase):
						model.train()
						for step, batch in enumerate(_UpperCAmelCase):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device)
									SCREAMING_SNAKE_CASE							=			model(**_UpperCAmelCase)
									SCREAMING_SNAKE_CASE							=			outputs.loss
									SCREAMING_SNAKE_CASE							=			loss / gradient_accumulation_steps
									accelerator.backward(_UpperCAmelCase)
									if step % gradient_accumulation_steps == 0:
												optimizer.step()
												lr_scheduler.step()
												optimizer.zero_grad()

						model.eval()
						for step, batch in enumerate(_UpperCAmelCase):
									# We could avoid this line since we set the accelerator with `device_placement=True`.
									batch.to(accelerator.device)
									with torch.no_grad():
												SCREAMING_SNAKE_CASE							=			model(**_UpperCAmelCase)
									SCREAMING_SNAKE_CASE							=			outputs.logits.argmax(dim=-1)
									SCREAMING_SNAKE_CASE					,    SCREAMING_SNAKE_CASE							=			accelerator.gather_for_metrics((predictions, batch['labels']))
									metric.add_batch(
									    predictions=_UpperCAmelCase				,							references=_UpperCAmelCase				,							)

						SCREAMING_SNAKE_CASE							=			metric.compute()
						# Use accelerator.print to print only on the main process.
						accelerator.print(F'''epoch {epoch}:'''				,							_UpperCAmelCase)






def 					lowerCamelCase__					():
			SCREAMING_SNAKE_CASE							=			argparse.ArgumentParser(description='Simple example of training script.')
			parser.add_argument(
			    '--mixed_precision'				,							type=_UpperCAmelCase				,							default=_UpperCAmelCase				,							choices=['no', 'fp16', 'bf16', 'fp8']				,							help='Whether to use mixed precision. Choose'
			    'between fp16 and bf16 (bfloat16). Bf16 requires PyTorch >= 1.10.'
			    'and an Nvidia Ampere GPU.'				,							)
			parser.add_argument('--cpu'				,							action='store_true'				,							help='If passed, will train on the CPU.')
			SCREAMING_SNAKE_CASE							=			parser.parse_args()
			SCREAMING_SNAKE_CASE							=			{'lr': 2e-5, 'num_epochs': 3, 'seed': 42, 'batch_size': 16}
			training_function(_UpperCAmelCase				,							_UpperCAmelCase)


if __name__ == "__main__":
				main()










'''simple docstring'''


import copy
from dataclasses import dataclass, field
from typing import ClassVar, Dict

from ..features import Audio, ClassLabel, Features
from .base import TaskTemplate





@dataclass(frozen=__magic_name__			)
class  __lowercase    (	__magic_name__			):
   _a    						=   field(default="""audio-classification"""							,				metadata={"""include_in_asdict_even_if_is_default""": True}			)
   _a    						=   Features({"""audio""": Audio()}			)
   _a    						=   Features({"""labels""": ClassLabel}			)
   _a    						=   "audio"
   _a    						=   "labels"







   def 		UpperCamelCase__      (						self   ,				UpperCamelCase   )					->     Dict:
     if self.label_column not in features:
       raise ValueError(f"Column {self.label_column} is not present in features."   )
     if not isinstance(features[self.label_column]   ,				UpperCamelCase   ):
       raise ValueError(f"Column {self.label_column} is not a ClassLabel."   )
     __a								=       copy.deepcopy(self   )
     __a								=       self.label_schema.copy()
     __a								=       features[self.label_column]
     __a								=       label_schema
     return task_template







   @property
   def 		UpperCamelCase__      (						self   )					->     Dict[str, str]:
     return {
         self.audio_column: "audio",
         self.label_column: "labels",
     }



